Artificial Intelligence/Machine Learning-Driven cfDNA Methylation Blood Test for Early Cancer Detection in Military Personnel With Environmental Toxin Exposure.
Military personnel face heightened cancer risks from exposure to burn-pit emissions, per- and polyfluoroalkyl substances (PFAS), jet fuel, and radiation. Veterans show elevated malignancy rates, and active-duty personnel remain vulnerable because of ongoing operational exposures, underscoring the need for early detection. Traditional screening methods are invasive and have limited sensitivity for early-stage disease. This study evaluates the feasibility of artificial intelligence/machine learning (AI/ML)-based cell-free DNA (cfDNA) methylation analysis for early cancer detection using publicly available civilian datasets, to inform future application in high-risk military populations.
A Machine-Learning driven cfDNA methylation classifier framework was developed for early, minimally-invasive detection of cancers associated with toxic and occupational exposures. The platform uses bisulfite-sequenced data to identify differential methylation signatures that distinguish early stage hepatocellular carcinoma (HCC) from chronic liver disease and healthy controls. Proof-of-concept studies were conducted on public datasets for hepatocellular carcinoma (HCC) and esophageal cancer. Three cfDNA datasets (GSE93203, GSE63775, and PRJCA001372) were used for model training and k-fold cross-validation, and an independent WGBS liver-tissue dataset (PRJNA984754) served as a blind cross-assay validation set. Framework extensibility to other cancers was assessed through the EpiPanGI-Dx esophageal-cancer dataset (ESCC training, EAC validation).
Across 3 HCC models, internal cross-validation achieved 84%-94% accuracy (AUC 0.80-0.88). On the independent WGBS dataset validated against the 3 trained models, classifiers generalized with 83%-100% accuracy (AUC 0.80-1.00). Clinical validation using biobank cfDNA plasma samples from early-stage hepatocellular carcinoma and cirrhosis controls is ongoing. The esophageal cancer model reproduced published performance (AUC 0.94 for ESCC, 0.90 for EAC), demonstrating generalizability beyond HCC.
The cfDNA methylation-based AI/ML platform is promising for early multi-cancer detection in environmentally exposed military populations. Early results demonstrate feasibility and justify expansion to additional cancers, and larger clinical validation studies, potentially enhancing survivability through earlier intervention.
A Machine-Learning driven cfDNA methylation classifier framework was developed for early, minimally-invasive detection of cancers associated with toxic and occupational exposures. The platform uses bisulfite-sequenced data to identify differential methylation signatures that distinguish early stage hepatocellular carcinoma (HCC) from chronic liver disease and healthy controls. Proof-of-concept studies were conducted on public datasets for hepatocellular carcinoma (HCC) and esophageal cancer. Three cfDNA datasets (GSE93203, GSE63775, and PRJCA001372) were used for model training and k-fold cross-validation, and an independent WGBS liver-tissue dataset (PRJNA984754) served as a blind cross-assay validation set. Framework extensibility to other cancers was assessed through the EpiPanGI-Dx esophageal-cancer dataset (ESCC training, EAC validation).
Across 3 HCC models, internal cross-validation achieved 84%-94% accuracy (AUC 0.80-0.88). On the independent WGBS dataset validated against the 3 trained models, classifiers generalized with 83%-100% accuracy (AUC 0.80-1.00). Clinical validation using biobank cfDNA plasma samples from early-stage hepatocellular carcinoma and cirrhosis controls is ongoing. The esophageal cancer model reproduced published performance (AUC 0.94 for ESCC, 0.90 for EAC), demonstrating generalizability beyond HCC.
The cfDNA methylation-based AI/ML platform is promising for early multi-cancer detection in environmentally exposed military populations. Early results demonstrate feasibility and justify expansion to additional cancers, and larger clinical validation studies, potentially enhancing survivability through earlier intervention.